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---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
datasets:
- mediasum
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: Bart_mediasum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: mediasum
type: mediasum
config: roberta_prepended
split: validation
args: roberta_prepended
metrics:
- name: Rouge1
type: rouge
value: 0.3236
- name: Precision
type: precision
value: 0.8858
- name: Recall
type: recall
value: 0.8739
- name: F1
type: f1
value: 0.8795
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bart_mediasum
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the mediasum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9021
- Rouge1: 0.3236
- Rouge2: 0.1651
- Rougel: 0.2953
- Rougelsum: 0.2953
- Gen Len: 15.7946
- Precision: 0.8858
- Recall: 0.8739
- F1: 0.8795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 2.1171 | 1.0 | 4621 | 2.0135 | 0.3138 | 0.1556 | 0.2853 | 0.2853 | 16.4704 | 0.8836 | 0.8717 | 0.8773 |
| 1.9804 | 2.0 | 9242 | 1.9440 | 0.3147 | 0.1581 | 0.2864 | 0.2866 | 16.2207 | 0.8831 | 0.8725 | 0.8775 |
| 1.8971 | 3.0 | 13863 | 1.9157 | 0.3209 | 0.1638 | 0.2925 | 0.2926 | 15.4676 | 0.8857 | 0.8733 | 0.8792 |
| 1.8449 | 4.0 | 18484 | 1.9021 | 0.3236 | 0.1651 | 0.2953 | 0.2953 | 15.7946 | 0.8858 | 0.8739 | 0.8795 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
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